In their own words: what bothers children online?

In an open-ended survey question to European 9- to 16-year-olds, some 10,000 children reported a range of risks that concern them on the internet. Pornography (named by 22% of children who mentioned risks), conduct risk such as cyber-bullying (19%) and violent content (18%) were at the top of children’s concerns. The priority given to violent content is noteworthy insofar as this receives less attention than sexual content or bullying in awareness-raising initiatives. Many children express shock and disgust on witnessing violent, aggressive or gory online content, especially that which graphically depicts realistic violence against vulnerable victims, including from the news. Video-sharing websites such as YouTube were primary sources of violent and pornographic content. The findings discussed in relation to children’s fear responses to screen media and the implications for the public policy agenda on internet safety are identified

Some like it bad: testing a model on perceiving and experiencing fictional characters

We developed an encompassing theory that explains how readers of fiction and spectators of motion pictures establish affective relationships with fictional characters (FCs). The perceiving and experiencing fictional characters (PEFiC) theory is anchored in art perception, psychological aesthetics, and social and emotion psychology and addresses both the complexity and intrinsic affectivity involved in media exposure. In a between-subject design (N = 312), engagement and appreciation were measured as a function of the ethics (good vs. bad), aesthetics (beautiful vs. ugly), and epistemics (realistic vs. unrealistic) of eight protagonists in feature movies. The PEFiC model best fit the data with a unipolarity of factors and outperformed traditional theories (identification, empathy): The trade-off between involvement and distance explained the appreciation of FCs better than either distance or involvement alone. The mediators similarity, relevance, and valence exerted significant (interaction) effects, thus complicating the results. Furthermore, the effects of mediated bad persons differed strongly from ethically good ones. Copyright © 2005, Lawrence Erlbaum Associates, Inc

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism